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Search has been vital to artificial intelligence from the very beginning as a core technique in problem solving. The authors present a thorough overview of heuristic search with a balance of discussion between theoretical analysis and efficient implementation and application to real-world problems. Current developments in search such as pattern databases and search with efficient use of external memory and parallel processing units on main boards and graphics cards are detailed.

Heuristic search as a problem solving tool is demonstrated in applications for puzzle solving, game playing, constraint satisfaction and machine learning. While no previous familiarity with heuristic search is necessary the reader should have a basic knowledge of algorithms, data structures, and calculus. Real-world case studies and chapter ending exercises help to create a full and realized picture of how search fits into the world of artificial intelligence and the one around us.

Reviews

"Heuristic Search is a very solid monograph and textbook on (not only heuristic) search. In its presentation it is always more formal than colloquial, it is precise and well structured. Due to its spiral approach it motivates reading it in its entirety." --Zentralblatt MATH 2012

"The authors have done an outstanding job putting together this book on artificial intelligence (AI) heuristic state space search. It comprehensively covers the subject from its basics to the most recent work and is a great introduction for beginners in this field." --BCS.org

"Heuristic search lies at the core of Artificial Intelligence and it provides the foundations for many different approaches in problem solving. This book provides a comprehensive yet deep description of the main algorithms in the field along with a very complete discussion of their main applications. Very well-written, it embellishes every algorithm with pseudo-code and technical studies of their theoretical performance." --Carlos Linares Lopez, Universidad Carlos III de Madrid

"This is an introduction to artificial intelligence heuristic state space search. Authors Edelkamp (U. of Bremen, Germany) and Schrodl (a research scientist at Yahoo! Labs) seek to strike a balance between search algorithms and their theoretical analysis, on the one hand, and their efficient implementation and application to important real-world problems on the other, while covering the field comprehensively from well-known basic results to recent work in the state of the art. Prior knowledge of artificial intelligence is not assumed, but basic knowledge of algorithms, data structures, and calculus is expected. Proofs are included for formal rigor and to introduce proof techniques to the reader. They have organized the material into five sections: heuristic search primer, heuristic search under memory constraints, heuristic search under time constraints, heuristic search variants, and applications." --SciTech Book News

"This almost encyclopedic text is suitable for advanced courses in artificial intelligence and as a text and reference for developers, practitioners, students, and researchers in artificial intelligence, robotics, computational biology, and the decision sciences. The exposition is comparable to texts for a graduate-level or advanced undergraduate course in computer science, and prior exposure or coursework in advanced algorithms, computability, or artificial intelligence would help a great deal in understanding the material. Algorithms are described in pseudocode, accompanied by diagrams and narrative explanations in the text. The vast size of the ‘search algorithms’ subject domain and the variety of applications of search mean that much information--especially pertaining to applications of search algorithms--had to be left out; however, an extensive (though still limited) bibliography is included for follow-up by the reader. Exercises are provided for each chapter, except the five chapters on applications, and bibliographic notes accompany all chapters." --Computing Reviews

Feature

• Provides real-world success stories and case studies for heuristic search algorithms
• Includes many AI developments not yet covered in textbooks such as pattern databases, symbolic search, and parallel processing units

정보제공 :

PART I Heuristic Search Primer  Chapter 1 Introduction Chapter 2 Basic Search Algorithms  Chapter 3 Dictionary Data Structures  Chapter 4 Automatically Created Heuristics
 
PART II Heuristic Search under Memory Constraints Chapter 5 Linear-Space Search Chapter 6 Memory Restricted Search  Chapter 7 Symbolic Search  Chapter 8 External Search 
 
PART III Heuristic Search under Time Constraints  Chapter 9 Distributed Search  Chapter 10 State Space Pruning Chapter 11 Real-Time Search by Sven Koenig 
 
PART IV Heuristic Search Variants  Chapter 12 Adversary Search  Chapter 13 Constraint Search  Chapter 14 Selective Search
 
PART V Heurstic Search Applications Chapter 15 Action Planning  Chapter 16 Automated System Verification  Chapter 17 Vehicle Navigation  Chapter 18 Computational Biology  Chapter 19 Robotics by Sven Koenig 

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